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Home / Journals / IASC / Online First / doi:10.32604/iasc.2024.042841
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A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

Fangzhen Ge1,3, Yating Wu1,*, Debao Chen2,4, Longfeng Shen1,5
1 School of Computer Science and Technology, Huaibei Normal University, Huaibei, 340604, China
2 School of Physic and Electronic Information, Huaibei Normal University, Huaibei, 340604, China
3 Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), Huaibei Normal University, Huaibei, 340604, China
4 Anhui Province Key Laboratory of Intelligent Computing and Applications, Huaibei Normal University, Huaibei, 340604, China
5 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230000, China
* Corresponding Author: Yating Wu. Email: email
(This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)

Intelligent Automation & Soft Computing https://doi.org/10.32604/iasc.2024.042841

Received 14 June 2023; Accepted 31 January 2024; Published online 28 March 2024

Abstract

It is still a huge challenge for traditional Pareto-dominated many-objective optimization algorithms to solve many-objective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front, resulting in poor performance of those algorithms. For this reason, we propose a reference vector-assisted algorithm with an adaptive niche dominance relation, for short MaOEA-AR. The new dominance relation forms a niche based on the angle between candidate solutions. By comparing these solutions, the solution with the best convergence is found to be the non-dominated solution to improve the selection pressure. In reproduction, a mutation strategy of -bit crossover and hybrid mutation is used to generate high-quality offspring. On 23 test problems with up to 15-objective, we compared the proposed algorithm with five state-of-the-art algorithms. The experimental results verified that the proposed algorithm is competitive.

Keywords

Many-objective optimization; evolutionary algorithm; Pareto dominance; reference vector; adaptive niche

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